Load data

Notes:

Identified participants that may need cleaning by looking at ACF and also jumps (where jump is classified if absolute value of difference between two consecutive points is greater than 10. )

To manually clean

Removal function

001-004post

## Estimating learning rate. Each dot corresponds to a loss evaluation. 
## qu = 0.5................done

001-013post

## Estimating learning rate. Each dot corresponds to a loss evaluation. 
## qu = 0.5...............done

67pre2

## Estimating learning rate. Each dot corresponds to a loss evaluation. 
## qu = 0.5..........done

## Estimating learning rate. Each dot corresponds to a loss evaluation. 
## qu = 0.5..............done

001-013pre2

** Left Eye 

## Estimating learning rate. Each dot corresponds to a loss evaluation. 
## qu = 0.5.........done

** Right Eye 

## Estimating learning rate. Each dot corresponds to a loss evaluation. 
## qu = 0.5................done

001-030post

## Estimating learning rate. Each dot corresponds to a loss evaluation. 
## qu = 0.5................done

001-014post

## Estimating learning rate. Each dot corresponds to a loss evaluation. 
## qu = 0.5.............done

16post ; Removing Right eye as it is difficult to discern true curve

## 18post

## Estimating learning rate. Each dot corresponds to a loss evaluation. 
## qu = 0.5............done

## Estimating learning rate. Each dot corresponds to a loss evaluation. 
## qu = 0.5............done

27post

** removing left eye as true curve is difficult to discern

28pre2

** removing both eyes

34pre2

** Left Eye

72 post ; Removal of Right eye

57 post

; removal of both eyes

72 pre2

## Estimating learning rate. Each dot corresponds to a loss evaluation. 
## qu = 0.5.........done

77 pre2

** Left Eye

## Estimating learning rate. Each dot corresponds to a loss evaluation. 
## qu = 0.5.................done

88 post ;

## Estimating learning rate. Each dot corresponds to a loss evaluation. 
## qu = 0.5................done

117post remove one eye

004 post

## Estimating learning rate. Each dot corresponds to a loss evaluation. 
## qu = 0.5................done

9 post

## Estimating learning rate. Each dot corresponds to a loss evaluation. 
## qu = 0.5........done

9 pre2

## Estimating learning rate. Each dot corresponds to a loss evaluation. 
## qu = 0.5................done

11 post

** Removing left

  • Right Eye

## Estimating learning rate. Each dot corresponds to a loss evaluation. 
## qu = 0.5................done

65 prost

  • removal of right eye

13 pre 2

## Estimating learning rate. Each dot corresponds to a loss evaluation. 
## qu = 0.5........done

## Estimating learning rate. Each dot corresponds to a loss evaluation. 
## qu = 0.5................done

15 post

## Estimating learning rate. Each dot corresponds to a loss evaluation. 
## qu = 0.5..................done

102pre2

## Estimating learning rate. Each dot corresponds to a loss evaluation. 
## qu = 0.5........done

14 post

## Estimating learning rate. Each dot corresponds to a loss evaluation. 
## qu = 0.5........done

16 post

  • Removing One Eye

18 post

## Estimating learning rate. Each dot corresponds to a loss evaluation. 
## qu = 0.5............done

19 post

  • Removing Right Eye

60 pre2

** removal right eye

  • clean left

## Estimating learning rate. Each dot corresponds to a loss evaluation. 
## qu = 0.5.........done

61pre2

## Estimating learning rate. Each dot corresponds to a loss evaluation. 
## qu = 0.5..........done

21 pre2

## Estimating learning rate. Each dot corresponds to a loss evaluation. 
## qu = 0.5.................done

34 post ; REMOVE

34 pre2

71 pre 2 / post

  • Removing both

73 post

## Estimating learning rate. Each dot corresponds to a loss evaluation. 
## qu = 0.5.................done

74 post

## Estimating learning rate. Each dot corresponds to a loss evaluation. 
## qu = 0.5..................done

## Estimating learning rate. Each dot corresponds to a loss evaluation. 
## qu = 0.5.......................done

78 post

## Estimating learning rate. Each dot corresponds to a loss evaluation. 
## qu = 0.5........done

** Right eye ; removal

84post

95 post ; removal

95pre 2

005pre2 removal right eye

004pre2

## Estimating learning rate. Each dot corresponds to a loss evaluation. 
## qu = 0.5................done

25 post

** removing left eye as true curve is difficult to discern

Post manual verification meeting

Participant 1

  • Ben’s thoughts on keep or discard: Probably would remove both right and left eye here. Possible to keep one or both but would be a rougher estimate.

  • Suspected Reason: Pretty constant blinking

  • Decision: Remove both

Participant 2

  • Ben’s thoughts on keep or discard: Both eyes might be salvageable but probably. Would at the least keep the left eye

  • Suspected Reason: Very small pupil size makes for difficult estimates

  • Decision: Keep both eyes

## Estimating learning rate. Each dot corresponds to a loss evaluation. 
## qu = 0.5.........done

## Estimating learning rate. Each dot corresponds to a loss evaluation. 
## qu = 0.5.................done

Participant 3

  • Ben’s thoughts on keep or discard: Left eye might be salvageable but probably throw out both eyes

  • Suspected Reason: Very heavy eye liner creating false estimates for size/center of pupil

  • Decision: drop both eyes

Participant 4

  • Ben’s thoughts on keep or discard: Left eye should be removed. Right eye should probably be removed.

  • Suspected Reason: Pretty much constant blinking once the test begins

  • Decision: Drop both eyes

Participant 5

  • Ben’s thoughts on keep or discard: Should be able to keep left eye. The points that match with the right eye are pretty visible and would make a good estimate for true curve.

  • Suspected Reason: Iris on the left eye is slightly darker and the eye lashes are much darker, likely throwing off the distribution in color.

** Decision: Keep both eyes

## Estimating learning rate. Each dot corresponds to a loss evaluation. 
## qu = 0.5...............done

Participant 6

  • Tricky for left eye

  • Ben’s thoughts on keep or discard: A Lot of variation in the right eye, may be worth just removing. If Keep, it seems the top line is probably closer to the truth, higher density there and closer matches the right eye

  • Suspected Reason: Quite a bit of blinking and darker iris

  • Decision: Keep right eye only

Participant 7

  • Ben’s thoughts on keep or discard: A Lot of variation in the right eye, may be worth just removing.

  • Suspected Reason: Quite a bit of blinking and darker iris

60 post

** Ask Julia ; wild little intermission

## Estimating learning rate. Each dot corresponds to a loss evaluation. 
## qu = 0.5................done

## Estimating learning rate. Each dot corresponds to a loss evaluation. 
## qu = 0.5................done

Saving Fully Cleaned Data

Note that this will write over the dataset saved before manual cleaning